This is a little bit different than actually literally I vector centrality but it's very similar and so we call this technique IGEN reviews and I've become convinced that this is more or less the right way to do performance reviews.
And that there might come a moment, while reading a new, just-edited-for-the-thousandth-time draft, where that true self, or voice, pops out at you, suddenly revealed, as if someone else, not you, had done it.
But it's also your instruction budget is like if you give the model too many instructions and especially too many conflicting instructions and that's in your initial prompt and also like if you have a conversation you start going down a path and then you change your mind and you start going down a different you actually I don't want to do any of that I want to do this. It's like a it's a lot of computation the model has to do to notice that it has to ignore that whole thing.
So a a big challenge in object-oriented programming is dividing the responsibilities because you're moving the computation to where the data is. Saying, "Well, this object does this and that object does that is a really critical decision because you want to you want the computation near to the data so that there's less coupling between them." which is a lesson that I I think uh kind of got lost in the noise. That's the fundamental design move in ob in designing object-oriented programs and I I think I stand behind that.
And I think a good exposition you care a little bit less about like correctness on the way, but you can like deliberately craft things that are a little bit wrong that you correct along the way that gets like edited out in a crowd source environment.
Ideas like public key cryptography are I mean, they're just incredibly deep um very non-obvious ideas uh which in some sense lay hidden uh already sort of in in the 1930s.
People will sometimes talk about um you know, a theory of everything as a potential goal for for physics. And and then there's this presumption somehow that physics is done once you get there. And of course, this is this is not true at all if you think about computer science.
And so when you take these things in combination, I am absolutely certain that the world's GDP is going to accelerate in growth. I'm absolutely certain the percentage of that GDP that will be used for computation will be 100 times more than the past
So my betting is that it's mostly it is just classical computing that's going on in the brain, which suggests that all the phenomena are modelable or mimicable by a classical computer.
The Rattle Bag edited by Seamus Heaney and Ted Hughes - In an age of LLM-generated poetry and machine learning curation (even coming from yours truly sometimes), it's refreshing to have real people who love poetry curate it and show you what you need to read to touch grass.
There’s this assumption that computation is at the base of reality, and I see it at the top of reality, not at the base, because I think computation was built by our biosphere. It’s something that happened after many billion years of evolution. It doesn’t happen in every physical object.
Covers a few theory of computation highlights. The explanations are delightful and I've watched some of the videos more than once just to watch Bernhardt explain things.
For the latter I use nvALT almost exclusively, though my notes are all stored as individual text files (with OpenMeta tags) and can be added to, edited and searched from a plethora of apps and utilities.
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